code: def text_to_graph(text):
      import networkx as nx
      from sklearn.feature_extraction.text import TfidfVectorizer
      from sklearn.neighbors import kneighbors_graph
  
      # use tfidf to transform texts into feature vectors
      vectorizer = TfidfVectorizer()
      vectors = vectorizer.fit_transform(text)
  
      # build the graph which is full-connected
      N = vectors.shape[0]
      mat = kneighbors_graph(
          vectors,
          N,
          metric="cosine",
          mode="distance",
          include_self=True,
      )
      mat.data = 1 - mat.data  # to similarity
  
      g = nx.from_scipy_sparse_matrix(mat, create_using=nx.Graph())
  
      return g
  
file_path: OpenNE/visualization_example/20newsgroup.py
imports: import pandas
  import tensorflow
  import numpy
  from tensorflow.contrib.tensorboard.plugins import projector
  import os
  from openne.lap import LaplacianEigenmaps
  from openne.graph import Graph
  from openne import node2vec
label_desc: Use image node_id 2 for calling a global function with example usage: text_to_graph(text) and returns: g